How to Evaluate AI Assistants for Security Operations

Retrospective: this article looks back at events from March 2023, written in 2026 with the benefit of hindsight.

AI assistants for security operations can speed up triage and investigation — or add cost and noise. Here is a practical way to evaluate them.

Step 1: Define the use cases

Pick three to five tasks where your team loses time, for example:

• Summarizing multi-alert incidents.
• Writing and explaining KQL or SQL queries.
• Analyzing suspicious scripts or emails.
• Drafting incident reports for leadership.
• Answering "what is this device/user's recent activity?"

Step 2: Set success measures

• Time to triage or close incidents.
• Accuracy of answers (reviewed by senior analysts).
• Analyst satisfaction.
• Cost per incident or per month.

Step 3: Check data handling

• What data is sent to the model, and where is it processed?
• Is your data used to train models? (Most enterprise offerings say no — get it in writing.)
• What role-based access controls apply? Can the assistant see data the analyst can't?
• Are prompts and responses logged for audit?

Step 4: Run a time-boxed pilot

Four to six weeks with a few analysts on real (or replayed) incidents. Compare against a baseline.

Step 5: Watch for failure modes

• Hallucinated indicators or incorrect conclusions.
• Over-reliance by junior analysts.
• Prompt injection through data being analyzed (for example, malicious content in emails or logs that tries to manipulate the AI).

Step 6: Decide with numbers

Adopt where measurable improvement exceeds cost; keep humans approving actions.